AI Foundation Model Engineer (LLM / Agentic AI / Full-Stack AI Engineering)

AI Foundation Model Engineer (LLM / Agentic AI / Full-Stack AI Engineering)

Location: 210 Hudson Street, Jersey City, NJ, 07311 (3-4 days onsite per week) Interview: May require an in-person (F2F) interview Duration: 12 Month About the Role

We are seeking a Senior AI Foundation Model Engineer to design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation (RAG), and agentic workflows. This role converts AI concepts into secure, scalable, observable, and supportable production systems on our enterprise AI-ready platform (AIRP), currently AWS-hosted while following a cloud-agnostic architecture blueprint. Must Have:

Hands-on AWS AI and cloud engineering experience is a major asset, as AIRP currently runs on AWS. Comfort working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines. Experience mapping to business AI use cases such as KYC, credit underwriting, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening. Primary Ownership

Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP. End-to-end LLMOps/MLOps lifecycle — from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement. Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns federated across multiple business use cases. Key Responsibilities

Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems. Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns. Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns. Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures. Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques. Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience. Implement model and application observability — prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health. Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery. Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records. Must-Have Qualifications

7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning. Strong AWS cloud engineering experience. AWS AI/ML or GenAI exposure, including Bedrock, SageMaker, LLMs, RAG, embeddings, model serving, or AI platform engineering. Strong Terraform / Infrastructure as Code experience, including creating reusable cloud-agnostic IaC templates/modules. Strong DevOps / CI/CD pipeline experience, especially moving Terraform code through deployment pipelines. Enterprise platform engineering experience in regulated environments. Security, governance, audit, compliance, and Responsible AI awareness. Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns. Strong Python engineering skills with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks. Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms. Preferred Experience

Banking, risk, compliance, financial crime, operations, or enterprise technology background, with exposure to use cases such as: KYC Credit underwriting Governance tracking Pitch book generation Banker 360 / Customer 360 Deal library intelligence Financial crime quality Sanctions screening Experience with AWS Bedrock, SageMaker, OpenSearch, Kendra, Lambda, EKS/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways. Exposure to cloud-agnostic application patterns, reusable IaC modules, model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments. Citizen development / Microsoft stack experience (for relevant roles): Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, Power BI.